Dataset Information

Assessment of a Person-Level Risk Calculator to Predict New-Onset Bipolar Spectrum Disorder in Youth at Familial Risk.

ABSTRACT:

Importance

Early identification of individuals at high risk for the onset of bipolar spectrum disorder (BPSD) is key from both a clinical and research perspective. While previous work has identified the presence of a bipolar prodrome, the predictive implications for the individual have not been assessed, to date.

Objective

To build a risk calculator to predict the 5-year onset of BPSD in youth at familial risk for BPSD.

Design, setting, and participants

The Pittsburgh Bipolar Offspring Study is an ongoing community-based longitudinal cohort investigation of offspring of parents with bipolar I or II (and community controls), recruited between November 2001 and July 2007, with a median follow-up period of more than 9 years. Recruitment has ended, but follow-up is ongoing. The present analysis included offspring of parents with bipolar I or II (aged 6-17 years) who had not yet developed BPSD at baseline.

Main outcomes and measures

This study tested the degree to which a time-to-event model, including measures of mood and anxiety, general psychosocial functioning, age at mood disorder onset in the bipolar parent, and age at each visit, predicted new-onset BPSD. To fully use longitudinal data, the study assessed each visit separately, clustering within individuals. Discrimination was measured using the time-dependent area under the curve (AUC), predicting 5-year risk; internal validation was performed using 1000 bootstrapped resamples. Calibration was assessed by comparing observed vs predicted probability of new-onset BPSD.

Results

There were 412 at-risk offspring (202 [49.0%] female), with a mean (SD) visit age of 12.0 (3.5) years and a mean (SD) age at new-onset BPSD of 14.2 (4.5) years. Among them, 54 (13.1%) developed BPSD during follow-up (18 with BD I or II); these participants contributed a total of 1058 visits, 67 (6.3%) of which preceded new-onset BPSD within the next 5 years. Using internal validation to account for overfitting, the model provided good discrimination between converting vs nonconverting visits (AUC, 0.76; bootstrapped 95% CI, 0.71-0.82). Important univariate predictors of outcome (AUC range, 0.66-0.70) were dimensional measures of mania, depression, anxiety, and mood lability; psychosocial functioning; and parental age at mood disorder.

Conclusions and relevance

This risk calculator provides a practical tool for assessing the probability that a youth at familial risk for BPSD will develop new-onset BPSD within the next 5 years. Such a tool may be used by clinicians to inform frequency of monitoring and treatment options and for research studies to better identify potential participants at ultra high risk of conversion.

SUBMITTER: Hafeman DM

PROVIDER: S-EPMC5710639 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:OBJECTIVE:Adults with established diagnoses of serious mental illness (bipolar disorder and schizophrenia) exhibit structural brain abnormalities, yet less is known about how such abnormalities manifest earlier in development. METHOD:Cross-sectional data publicly available from the Philadelphia Neurodevelopmental Cohort (PNC) were analyzed. Structural magnetic resonance neuroimaging data were collected on a subset of the PNC (N = 989; 9-22 years old). Cortical thickness, surface area (SA), and subcortical volumes were calculated. Study participants were assessed for psychiatric symptomatology using a structured interview and the following groups were created: typically developing (n = 376), psychosis spectrum (PS; n = 113), bipolar spectrum (BP; n = 117), and BP + PS (n = 109). Group and developmental differences in structural magnetic resonance neuroimaging measures were examined. In addition, the extent to which any structural aberration was related to neurocognition, global functioning, and clinical symptomatology was examined. RESULTS:Compared with other groups, PS youth exhibited significantly decreased SA in the orbitofrontal, cingulate, precentral, and postcentral regions. PS youth also exhibited deceased thalamic volume compared with all other groups. The strongest effects for precentral and posterior cingulate SA decreases were seen during early adolescence (13-15 years old) in PS youth. The strongest effects for decreases in thalamic volume and orbitofrontal and postcentral SA were observed in mid-adolescence (16-18 years) in PS youth. Across groups, better overall functioning was associated with increased lateral orbitofrontal SA. Increased postcentral SA was associated with better executive cognition and less severe negative symptoms in the entire sample. CONCLUSION:In a community-based sample, decreased cortical SA and thalamic volume were present early in adolescent development in youth with PS symptoms, but not in youth with BP symptoms or with BP and PS symptoms. These findings point to potential biological distinctions between PS and BP conditions, which could suggest additional biomarkers relevant to early identification.